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Exploring the attitudes and beliefs of women regarding the implementation of midwife-led care in India:a mixed methods study
Get PDFProblem: Despite the recent introduction of midwifery services in India to improve maternal and newborn healthcare, there is limited research on women's perspectives on midwife-led care. Background: The Government of India initiated midwifery services guidelines in 2018 to improve the quality of care for pregnant women and newborns across the country. It is important to develop evidence-based strategies which can optimise the implementation of these new midwifery services. Aim: This study explored women's attitudes and beliefs towards the implementation of midwife-led care in two southern states of India. Methods: A convergent mixed methods design was employed using an online questionnaire and semi-structured online interviews. Quantitative data was analysed using descriptive statistics and qualitative analysis used a framework approach. Data from both sources were then integrated through merging techniques. Findings: A total of 307 women completed the online survey, and 23 participated in in-depth interviews. The study highlighted inadequate knowledge of midwife-led care among women. Despite this, 60 % expressed optimism about its benefits. Key factors to women's acceptance included better understanding outcomes, having trust in midwives, receiving respectful care, and having autonomy in decision-making. They also required midwife-led birthing units would be clean, accessible, and well resourced. Discussion: Most participants perceived midwife-led care as beneficial, valuing its skilled, responsive and compassionate services. Conclusion: Insights from this study have implications for the implementation of midwife-led care which should consider the informational needs, safety standards and cultural contexts of women and their families living in both urban and rural areas of India.</p
Learning instruction-guided manipulation affordance via large models for embodied robotic tasks
No full textWe study the task of language instruction-guided robotic manipulation, in which an embodied robot is supposed to manipulate the target objects based on the language instructions. In previous studies, the predicted manipulation regions of the target object typically do not change with specification from the language instructions, which means that the language perception and manipulation prediction are separate. However, in human behavioral patterns, the manipulation regions of the same object will change for different language instructions. In this paper, we propose Instruction-Guided Affordance Net (IGANet) for predicting affordance maps of instruction-guided robotic manipulation tasks by utilizing powerful priors from vision and language encoders pre-trained on large-scale datasets. We develop a Vison-Language-Models(VLMs)-based data augmentation pipeline, which can generate a large amount of data automatically for model training. Besides, with the help of Large-Language-Models(LLMs), actions can be effectively executed to finish the tasks defined by instructions. A series of real-world experiments revealed that our method can achieve better performance with generated data. Moreover, our model can generalize better to scenarios with unseen objects and language instructions.</p
Socioeconomic deprivation and perinatal anxiety:an observational cohort study
Get PDFBackground: Women from areas of social deprivation and minority ethnic groups are more likely to experience poor physical health and have higher rates of mental health problems relative to women from less socially disadvantaged groups. However, very little research has examined this in relation to perinatal anxiety. The current study aims to determine prevalence, risk factors and desire for treatment for perinatal anxiety in three regions of the UK with diverse regional characteristics. Methods: Women completed measures of anxiety in early, mid-, late-pregnancy and postpartum. Participants were included from three regions of the UK: Region 1 = North East England & North Cumbria n = 512; Region 2 = London North Thames n = 665; Region 3 = West Midlands n = 705. Results: Prevalence of perinatal anxiety was lower in Region 1 (OR 0.63 95% CI 0.45 to 0.89) and Region 2 (OR 0.72 95% CI 0.52 to 0.98) relative to Region 3. Analysis showed the effect of neighbourhood socioeconomic deprivation on perinatal anxiety differed by region. In more affluent regions, living in a deprived neighbourhood had a greater impact on perinatal anxiety than living in a deprived neighbourhood in a deprived region. Other factors associated with risk of anxiety in the perinatal period included physical health problems and identifying as being from ‘mixed or multiple’ ethnic groups. Conclusions: Neighbourhood deprivation relative to regional deprivation is a better predictor of perinatal anxiety than either regional deprivation or neighbourhood deprivation alone. Women of mixed ethnic backgrounds and women with physical health problems may warrant more attention in terms of screening and support for perinatal anxiety. Self-reported desire for treatment was found to be low.</p
Fault detection and monitoring for electric pump motors
No full textPatent file GB2402869.8 Outcome of Innvoate KTP project
Compassionate and ethical practice learning
No full textThe theme of compassionate practice learning continues in this chapter, as we consider the role of quality enhancement processes within learning environments and how supervisors, assessors and educators can work collaboratively through partnership approaches. The experiences of dissatisfied learners are explored from the perspective of a constellation of factors that require students and apprentices to be supported, particularly when raising their concerns.</p
Nurses’ perception of uncertainty in clinical decision-making:a qualitative study
Get PDFBackground: Uncertainty is a common challenge for nurses in clinical decision-making, which can compromise patient care quality and safety. To address this issue, it is essential to understand how nurses perceive and cope with uncertainty in their practice. Aim: This study aimed to explore nurses’ perceptions of uncertainty in clinical decision-making using a qualitative approach. Methods: This study was conducted with a qualitative approach and conventional content analysis in 2020. Participants consisted of 17 nurses from different wards of teaching hospitals in Northwestern Iran, recruited using the purposive sampling method. Data were collected through semi-structured interviews and analyzed simultaneously with data collection (June to December 2020). The data were analyzed using the content analysis approach suggested by Wildemuth. Data were managed with MAXQDA10 software. The analysis revealed four main themes and ten subthemes that described the nurses' experiences of uncertainty in clinical decision-making. Results: The main themes were: difficult choice, difficult situation, insufficient judgment, and emotional burden. Conclusions: The study participants defined uncertainty in clinical decision-making as a difficult choice that occurs in difficult situations, which influenced their clinical judgment and emotional well-being. These findings provide valuable insights for developing interventions to help nurses manage uncertainty and improve their decision-making skills and safety.</p
Some thoughts and reflections on identity, teaching, and writing, and how they might affect one another
No full textThis chapter moves through a series of loosely linked reflections on (in no particular order, because the following are not separate from one another) politics, teaching, and writing, and it attempts to think through the ways in which each shapes the other and produces something we might be tempted to call and identity. The main ideas are: that the writer’s sense of self is, at least in part, forged in his writing; and that his teaching shapes his writing just as his writing shapes his teaching
Improving patient experience for people prescribed medicines with a risk of dependence or withdrawal: co-designed solutions using experience based co-design
Get PDFBackground: Significant concerns have been raised regarding how medications with a risk of dependence or withdrawal are managed and how care is experienced by patients. This study sought to co-design solutions to improve the experience of care for patients prescribed benzodiazepines, z-drugs, opioids for chronic non-cancer pain, gabapentinoids and antidepressants. Method: Twenty patients and fifteen healthcare professionals from five different GP practices were recruited to take part. The study used Experience Based Co-Design. Patients and healthcare professionals completed semi-structured interviews and took part in feedback groups and co-design workshops to collaboratively identify priorities for improvement and to co-design solutions to improve the experience of care. Results: Poor patient experience was common among people prescribed medications with a risk of dependence or withdrawal. Patients and healthcare professionals identified three main priority areas to improve the experience of care: (i) ensuring patients are provided with detailed information in relation to their medication, (ii) ensuring continuity of care for patients, and (iii) providing alternative treatment options to medication. Solutions to improve care were co-designed by patients and healthcare staff and implemented within participating GP practices to improve the experience of care. Conclusion: Good patient experience is a key element of quality care. This study highlights that the provision of in-depth medication related information, continuity of care and alternative treatment to medication are important to patients prescribed medicines with a risk of dependence or withdrawal. Improving these aspects of care should be a priority for future improvement and delivery plans.</p
Forced migration:a relational wellbeing approach
Get PDFEditorial. In this Special Issue, we consider the ways in which a relational wellbeing approach can be used to understand the lives and trajectories of refugees in general and young refugees in particular. We mainly focus on the lives of young adults who came to the global North as unaccompanied children—that is, without an adult responsible for them when they claimed asylum. Many of the papers report from the Drawing Together project (see https://www.drawingtogetherproject.org/, accessed on 11 January 2024). The project focus is on ‘relational wellbeing’ for young refugees—that is, wellbeing that is experienced through actions that repair and amplify a sense of responsibility they and other people have to each other. Hospitality and reciprocity emerge through small acts of fellowship. In time, these build patterns of exchanges between young refugees and those important to them, leading to a mutual sense of ‘having enough’, ‘being connected’, and ‘feeling good’ (White and Jha 2020). This is wellbeing as a shared endeavour. Overall, the project and many contributions in this Special Issue stand at the conjunction between fields of research into wellbeing and refugee studies. The papers span contexts and countries, offering a sense of an international array of experiences, joined by an issue of supra-national importance—that is, the ways interaction and relationality mediate the experiences of becoming and being a refugee
Enhancing text comprehension via fusing pre-trained language model with knowledge graph
No full textPre-trained language models (PLMs) such as BERT and GPTs capture rich linguistic and syntactic knowledge from pre-training over large-scale text corpora, which can be further fine-tuned for specific downstream tasks. However, these models still have limitations as they rely on knowledge gained from plain text and ignore structured knowledge such as knowledge graphs (KGs). Recently, there has been a growing trend of explicitly integrating KGs into PLMs to improve their performance. For instance, K-BERT incorporates KG triples as domain-specific supplements into input sentences. Nevertheless, we have observed that such methods do not consider the semantic relevance between the introduced knowledge and the original input sentence, leading to the issue of knowledge impurities. To address this issue, we propose a semantic matching-based approach that enriches the input text with knowledge extracted from an external KG. The architecture of our model comprises three components: the knowledge retriever (KR), the knowledge injector (KI), and the knowledge aggregator (KA). The KR, built upon the sentence representation learning model (i.e. CoSENT), retrieves triples with high semantic relevance to the input sentence from an external KG to alleviate the issue of knowledge impurities. The KI then integrates the retrieved triples from the KR into the input text by converting the original sentence into a knowledge tree with multiple branches, the knowledge tree is transformed into an accessible sequence of text that can be fed into the KA. Finally, the KA takes the flattened knowledge tree and passes it through an embedding layer and a masked Transformer encoder. We conducted extensive evaluations on eight datasets covering five text comprehension tasks, and the experimental results demonstrate that our approach exhibits competitive advantages over popular knowledge-enhanced PLMs such as K-BERT and ERNIE
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